Overview

Dataset statistics

Number of variables17
Number of observations20000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.1 MiB
Average record size in memory474.7 B

Variable types

Text1
Categorical7
Numeric8
Boolean1

Alerts

years_of_employment has 550 (2.8%) zerosZeros

Reproduction

Analysis started2026-02-11 10:35:18.397197
Analysis finished2026-02-11 10:35:28.734956
Duration10.34 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

name
Text

Distinct200
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2026-02-11T10:35:28.972244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length13
Mean length11.05745
Min length8

Characters and Unicode

Total characters221149
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVihaan Sharma
2nd rowIshaan Patel
3rd rowKrishna Gupta
4th rowVihaan Singh
5th rowAditya Mehta
ValueCountFrequency (%)
iyer2085
 
5.2%
gupta2050
 
5.1%
sharma2033
 
5.1%
khan2023
 
5.1%
mehta2018
 
5.0%
reddy1994
 
5.0%
singh1986
 
5.0%
patel1960
 
4.9%
nair1943
 
4.9%
verma1908
 
4.8%
Other values (20)20000
50.0%
2026-02-11T10:35:29.362749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a42935
19.4%
20000
 
9.0%
n15099
 
6.8%
i14992
 
6.8%
r14953
 
6.8%
h14038
 
6.3%
y13045
 
5.9%
e10948
 
5.0%
S7943
 
3.6%
t7089
 
3.2%
Other values (20)60107
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)221149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a42935
19.4%
20000
 
9.0%
n15099
 
6.8%
i14992
 
6.8%
r14953
 
6.8%
h14038
 
6.3%
y13045
 
5.9%
e10948
 
5.0%
S7943
 
3.6%
t7089
 
3.2%
Other values (20)60107
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)221149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a42935
19.4%
20000
 
9.0%
n15099
 
6.8%
i14992
 
6.8%
r14953
 
6.8%
h14038
 
6.3%
y13045
 
5.9%
e10948
 
5.0%
S7943
 
3.6%
t7089
 
3.2%
Other values (20)60107
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)221149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a42935
19.4%
20000
 
9.0%
n15099
 
6.8%
i14992
 
6.8%
r14953
 
6.8%
h14038
 
6.3%
y13045
 
5.9%
e10948
 
5.0%
S7943
 
3.6%
t7089
 
3.2%
Other values (20)60107
27.2%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Driving License
5108 
Passport
5015 
Aadhar
5010 
PAN
4867 

Length

Max length15
Median length8
Mean length8.07005
Min length3

Characters and Unicode

Total characters161401
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPAN
2nd rowPAN
3rd rowAadhar
4th rowPAN
5th rowPAN

Common Values

ValueCountFrequency (%)
Driving License5108
25.5%
Passport5015
25.1%
Aadhar5010
25.1%
PAN4867
24.3%

Length

2026-02-11T10:35:29.491923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-11T10:35:29.595599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
driving5108
20.3%
license5108
20.3%
passport5015
20.0%
aadhar5010
20.0%
pan4867
19.4%

Most occurring characters

ValueCountFrequency (%)
i15324
 
9.5%
s15138
 
9.4%
r15133
 
9.4%
a15035
 
9.3%
e10216
 
6.3%
n10216
 
6.3%
P9882
 
6.1%
A9877
 
6.1%
v5108
 
3.2%
D5108
 
3.2%
Other values (10)50364
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)161401
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i15324
 
9.5%
s15138
 
9.4%
r15133
 
9.4%
a15035
 
9.3%
e10216
 
6.3%
n10216
 
6.3%
P9882
 
6.1%
A9877
 
6.1%
v5108
 
3.2%
D5108
 
3.2%
Other values (10)50364
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)161401
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i15324
 
9.5%
s15138
 
9.4%
r15133
 
9.4%
a15035
 
9.3%
e10216
 
6.3%
n10216
 
6.3%
P9882
 
6.1%
A9877
 
6.1%
v5108
 
3.2%
D5108
 
3.2%
Other values (10)50364
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)161401
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i15324
 
9.5%
s15138
 
9.4%
r15133
 
9.4%
a15035
 
9.3%
e10216
 
6.3%
n10216
 
6.3%
P9882
 
6.1%
A9877
 
6.1%
v5108
 
3.2%
D5108
 
3.2%
Other values (10)50364
31.2%

cibil_score
Real number (ℝ)

Distinct601
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean600.78785
Minimum300
Maximum900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2026-02-11T10:35:29.728130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile329
Q1449
median600
Q3753
95-th percentile872
Maximum900
Range600
Interquartile range (IQR)304

Descriptive statistics

Standard deviation174.34704
Coefficient of variation (CV)0.29019735
Kurtosis-1.2127606
Mean600.78785
Median Absolute Deviation (MAD)152
Skewness0.0038428634
Sum12015757
Variance30396.892
MonotonicityNot monotonic
2026-02-11T10:35:29.882429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41155
 
0.3%
71954
 
0.3%
43247
 
0.2%
36847
 
0.2%
74047
 
0.2%
57647
 
0.2%
85947
 
0.2%
72746
 
0.2%
41046
 
0.2%
57246
 
0.2%
Other values (591)19518
97.6%
ValueCountFrequency (%)
30042
0.2%
30128
0.1%
30230
0.1%
30324
0.1%
30427
0.1%
30525
0.1%
30629
0.1%
30737
0.2%
30830
0.1%
30938
0.2%
ValueCountFrequency (%)
90041
0.2%
89928
0.1%
89843
0.2%
89736
0.2%
89632
0.2%
89537
0.2%
89431
0.2%
89330
0.1%
89224
0.1%
89136
0.2%

age
Real number (ℝ)

Distinct44
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.53995
Minimum21
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2026-02-11T10:35:30.043002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q132
median43
Q353
95-th percentile62
Maximum64
Range43
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.660755
Coefficient of variation (CV)0.29762036
Kurtosis-1.1949317
Mean42.53995
Median Absolute Deviation (MAD)11
Skewness0.00094946869
Sum850799
Variance160.29472
MonotonicityNot monotonic
2026-02-11T10:35:30.189728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
58494
 
2.5%
50489
 
2.4%
33489
 
2.4%
47485
 
2.4%
43479
 
2.4%
49479
 
2.4%
31477
 
2.4%
25477
 
2.4%
61476
 
2.4%
46474
 
2.4%
Other values (34)15181
75.9%
ValueCountFrequency (%)
21417
2.1%
22454
2.3%
23445
2.2%
24469
2.3%
25477
2.4%
26428
2.1%
27456
2.3%
28437
2.2%
29445
2.2%
30438
2.2%
ValueCountFrequency (%)
64468
2.3%
63416
2.1%
62460
2.3%
61476
2.4%
60458
2.3%
59460
2.3%
58494
2.5%
57466
2.3%
56437
2.2%
55449
2.2%

annual_income
Real number (ℝ)

Distinct19912
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1360189.6
Minimum200073
Maximum2499681
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2026-02-11T10:35:30.344870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum200073
5-th percentile313902.25
Q1785526.25
median1369203
Q31934202.8
95-th percentile2387104.4
Maximum2499681
Range2299608
Interquartile range (IQR)1148676.5

Descriptive statistics

Standard deviation665740.28
Coefficient of variation (CV)0.48944667
Kurtosis-1.2034797
Mean1360189.6
Median Absolute Deviation (MAD)574452
Skewness-0.026908375
Sum2.7203792 × 1010
Variance4.4321011 × 1011
MonotonicityNot monotonic
2026-02-11T10:35:30.510364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13615383
 
< 0.1%
7349752
 
< 0.1%
20053312
 
< 0.1%
12454012
 
< 0.1%
16596032
 
< 0.1%
15519342
 
< 0.1%
15222632
 
< 0.1%
18759842
 
< 0.1%
2166252
 
< 0.1%
21246512
 
< 0.1%
Other values (19902)19979
99.9%
ValueCountFrequency (%)
2000731
< 0.1%
2001161
< 0.1%
2001331
< 0.1%
2004091
< 0.1%
2004351
< 0.1%
2012821
< 0.1%
2012861
< 0.1%
2016991
< 0.1%
2017591
< 0.1%
2017731
< 0.1%
ValueCountFrequency (%)
24996811
< 0.1%
24995821
< 0.1%
24995061
< 0.1%
24993531
< 0.1%
24991411
< 0.1%
24989252
< 0.1%
24988811
< 0.1%
24988651
< 0.1%
24988191
< 0.1%
24986931
< 0.1%

employment_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Self-Employed
6744 
Salaried
6669 
Business
6587 

Length

Max length13
Median length8
Mean length9.686
Min length8

Characters and Unicode

Total characters193720
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelf-Employed
2nd rowSalaried
3rd rowSalaried
4th rowSalaried
5th rowSelf-Employed

Common Values

ValueCountFrequency (%)
Self-Employed6744
33.7%
Salaried6669
33.3%
Business6587
32.9%

Length

2026-02-11T10:35:30.661978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-11T10:35:30.747278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
self-employed6744
33.7%
salaried6669
33.3%
business6587
32.9%

Most occurring characters

ValueCountFrequency (%)
e26744
13.8%
l20157
 
10.4%
s19761
 
10.2%
S13413
 
6.9%
d13413
 
6.9%
a13338
 
6.9%
i13256
 
6.8%
f6744
 
3.5%
-6744
 
3.5%
E6744
 
3.5%
Other values (8)53406
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)193720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e26744
13.8%
l20157
 
10.4%
s19761
 
10.2%
S13413
 
6.9%
d13413
 
6.9%
a13338
 
6.9%
i13256
 
6.8%
f6744
 
3.5%
-6744
 
3.5%
E6744
 
3.5%
Other values (8)53406
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)193720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e26744
13.8%
l20157
 
10.4%
s19761
 
10.2%
S13413
 
6.9%
d13413
 
6.9%
a13338
 
6.9%
i13256
 
6.8%
f6744
 
3.5%
-6744
 
3.5%
E6744
 
3.5%
Other values (8)53406
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)193720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e26744
13.8%
l20157
 
10.4%
s19761
 
10.2%
S13413
 
6.9%
d13413
 
6.9%
a13338
 
6.9%
i13256
 
6.8%
f6744
 
3.5%
-6744
 
3.5%
E6744
 
3.5%
Other values (8)53406
27.6%

years_of_employment
Real number (ℝ)

Zeros 

Distinct40
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.56895
Minimum0
Maximum39
Zeros550
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2026-02-11T10:35:30.862234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median20
Q330
95-th percentile38
Maximum39
Range39
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.563037
Coefficient of variation (CV)0.59088695
Kurtosis-1.1993497
Mean19.56895
Median Absolute Deviation (MAD)10
Skewness-0.013179532
Sum391379
Variance133.70383
MonotonicityNot monotonic
2026-02-11T10:35:31.007777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0550
 
2.8%
26535
 
2.7%
32527
 
2.6%
25526
 
2.6%
4525
 
2.6%
29525
 
2.6%
37524
 
2.6%
38522
 
2.6%
10518
 
2.6%
11516
 
2.6%
Other values (30)14732
73.7%
ValueCountFrequency (%)
0550
2.8%
1508
2.5%
2480
2.4%
3456
2.3%
4525
2.6%
5456
2.3%
6487
2.4%
7477
2.4%
8508
2.5%
9503
2.5%
ValueCountFrequency (%)
39498
2.5%
38522
2.6%
37524
2.6%
36507
2.5%
35467
2.3%
34486
2.4%
33493
2.5%
32527
2.6%
31499
2.5%
30492
2.5%

existing_loans
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size976.7 KiB
3
4092 
4
4078 
1
3996 
2
3938 
0
3896 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
34092
20.5%
44078
20.4%
13996
20.0%
23938
19.7%
03896
19.5%

Length

2026-02-11T10:35:31.152116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-11T10:35:31.250513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
34092
20.5%
44078
20.4%
13996
20.0%
23938
19.7%
03896
19.5%

Most occurring characters

ValueCountFrequency (%)
34092
20.5%
44078
20.4%
13996
20.0%
23938
19.7%
03896
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)20000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
34092
20.5%
44078
20.4%
13996
20.0%
23938
19.7%
03896
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
34092
20.5%
44078
20.4%
13996
20.0%
23938
19.7%
03896
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
34092
20.5%
44078
20.4%
13996
20.0%
23938
19.7%
03896
19.5%

loan_amount
Real number (ℝ)

Distinct19894
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1024454.7
Minimum50086
Maximum1999987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2026-02-11T10:35:31.398791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50086
5-th percentile147329.2
Q1531797.5
median1022587.5
Q31520187.8
95-th percentile1905603.9
Maximum1999987
Range1949901
Interquartile range (IQR)988390.25

Descriptive statistics

Standard deviation565980.48
Coefficient of variation (CV)0.55246999
Kurtosis-1.2160945
Mean1024454.7
Median Absolute Deviation (MAD)493617
Skewness0.0019372338
Sum2.0489094 × 1010
Variance3.203339 × 1011
MonotonicityNot monotonic
2026-02-11T10:35:31.560364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13363302
 
< 0.1%
10247122
 
< 0.1%
1525492
 
< 0.1%
8999802
 
< 0.1%
8412132
 
< 0.1%
6688412
 
< 0.1%
12609122
 
< 0.1%
12494732
 
< 0.1%
19080482
 
< 0.1%
18973612
 
< 0.1%
Other values (19884)19980
99.9%
ValueCountFrequency (%)
500861
< 0.1%
502311
< 0.1%
504481
< 0.1%
504971
< 0.1%
506411
< 0.1%
510351
< 0.1%
511911
< 0.1%
512411
< 0.1%
514531
< 0.1%
514891
< 0.1%
ValueCountFrequency (%)
19999871
< 0.1%
19998581
< 0.1%
19998511
< 0.1%
19998361
< 0.1%
19997711
< 0.1%
19997481
< 0.1%
19997401
< 0.1%
19995011
< 0.1%
19994671
< 0.1%
19992901
< 0.1%

loan_tenure_years
Real number (ℝ)

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.97795
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2026-02-11T10:35:31.695049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q322
95-th percentile28
Maximum29
Range28
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.3513503
Coefficient of variation (CV)0.55757632
Kurtosis-1.1992982
Mean14.97795
Median Absolute Deviation (MAD)7
Skewness-0.0016007385
Sum299559
Variance69.745051
MonotonicityNot monotonic
2026-02-11T10:35:31.824751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
10720
 
3.6%
8719
 
3.6%
5719
 
3.6%
1719
 
3.6%
23712
 
3.6%
18710
 
3.5%
11709
 
3.5%
21708
 
3.5%
16707
 
3.5%
6707
 
3.5%
Other values (19)12870
64.3%
ValueCountFrequency (%)
1719
3.6%
2682
3.4%
3658
3.3%
4677
3.4%
5719
3.6%
6707
3.5%
7645
3.2%
8719
3.6%
9659
3.3%
10720
3.6%
ValueCountFrequency (%)
29660
3.3%
28684
3.4%
27689
3.4%
26689
3.4%
25691
3.5%
24685
3.4%
23712
3.6%
22664
3.3%
21708
3.5%
20675
3.4%

interest_rate
Real number (ℝ)

Distinct1101
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.469395
Minimum7
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2026-02-11T10:35:31.973099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.52
Q19.7075
median12.44
Q315.21
95-th percentile17.45
Maximum18
Range11
Interquartile range (IQR)5.5025

Descriptive statistics

Standard deviation3.1874127
Coefficient of variation (CV)0.25561886
Kurtosis-1.2002985
Mean12.469395
Median Absolute Deviation (MAD)2.76
Skewness0.009684857
Sum249387.91
Variance10.159599
MonotonicityNot monotonic
2026-02-11T10:35:32.128305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.0932
 
0.2%
14.6731
 
0.2%
14.5231
 
0.2%
13.531
 
0.2%
14.0930
 
0.1%
10.3530
 
0.1%
7.9429
 
0.1%
17.5429
 
0.1%
13.4229
 
0.1%
15.7629
 
0.1%
Other values (1091)19699
98.5%
ValueCountFrequency (%)
710
 
0.1%
7.0118
0.1%
7.0217
0.1%
7.0313
0.1%
7.049
 
< 0.1%
7.0517
0.1%
7.0625
0.1%
7.0726
0.1%
7.0817
0.1%
7.0917
0.1%
ValueCountFrequency (%)
1811
0.1%
17.9921
0.1%
17.9822
0.1%
17.9717
0.1%
17.9622
0.1%
17.9517
0.1%
17.9426
0.1%
17.9313
0.1%
17.9216
0.1%
17.9117
0.1%

debt_to_income_ratio
Real number (ℝ)

Distinct51
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.350266
Minimum0.1
Maximum0.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2026-02-11T10:35:32.265227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.13
Q10.23
median0.35
Q30.48
95-th percentile0.57
Maximum0.6
Range0.5
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.14417104
Coefficient of variation (CV)0.41160444
Kurtosis-1.2054238
Mean0.350266
Median Absolute Deviation (MAD)0.13
Skewness-0.0011783475
Sum7005.32
Variance0.020785289
MonotonicityNot monotonic
2026-02-11T10:35:32.416936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.56448
 
2.2%
0.55441
 
2.2%
0.57432
 
2.2%
0.25431
 
2.2%
0.27426
 
2.1%
0.34425
 
2.1%
0.48422
 
2.1%
0.17420
 
2.1%
0.22420
 
2.1%
0.4417
 
2.1%
Other values (41)15718
78.6%
ValueCountFrequency (%)
0.1197
1.0%
0.11397
2.0%
0.12384
1.9%
0.13397
2.0%
0.14355
1.8%
0.15410
2.1%
0.16417
2.1%
0.17420
2.1%
0.18411
2.1%
0.19380
1.9%
ValueCountFrequency (%)
0.6176
 
0.9%
0.59379
1.9%
0.58362
1.8%
0.57432
2.2%
0.56448
2.2%
0.55441
2.2%
0.54380
1.9%
0.53391
2.0%
0.52409
2.0%
0.51409
2.0%

marital_status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Single
6747 
Married
6640 
Divorced
6613 

Length

Max length8
Median length7
Mean length6.9933
Min length6

Characters and Unicode

Total characters139866
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDivorced
2nd rowMarried
3rd rowSingle
4th rowDivorced
5th rowDivorced

Common Values

ValueCountFrequency (%)
Single6747
33.7%
Married6640
33.2%
Divorced6613
33.1%

Length

2026-02-11T10:35:32.557816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-11T10:35:32.641522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
single6747
33.7%
married6640
33.2%
divorced6613
33.1%

Most occurring characters

ValueCountFrequency (%)
i20000
14.3%
e20000
14.3%
r19893
14.2%
d13253
9.5%
S6747
 
4.8%
n6747
 
4.8%
l6747
 
4.8%
g6747
 
4.8%
a6640
 
4.7%
M6640
 
4.7%
Other values (4)26452
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)139866
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i20000
14.3%
e20000
14.3%
r19893
14.2%
d13253
9.5%
S6747
 
4.8%
n6747
 
4.8%
l6747
 
4.8%
g6747
 
4.8%
a6640
 
4.7%
M6640
 
4.7%
Other values (4)26452
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)139866
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i20000
14.3%
e20000
14.3%
r19893
14.2%
d13253
9.5%
S6747
 
4.8%
n6747
 
4.8%
l6747
 
4.8%
g6747
 
4.8%
a6640
 
4.7%
M6640
 
4.7%
Other values (4)26452
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)139866
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i20000
14.3%
e20000
14.3%
r19893
14.2%
d13253
9.5%
S6747
 
4.8%
n6747
 
4.8%
l6747
 
4.8%
g6747
 
4.8%
a6640
 
4.7%
M6640
 
4.7%
Other values (4)26452
18.9%

education_level
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
PhD
5071 
Master
5036 
Bachelor
4973 
High School
4920 

Length

Max length11
Median length8
Mean length6.96665
Min length3

Characters and Unicode

Total characters139333
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBachelor
2nd rowMaster
3rd rowBachelor
4th rowMaster
5th rowBachelor

Common Values

ValueCountFrequency (%)
PhD5071
25.4%
Master5036
25.2%
Bachelor4973
24.9%
High School4920
24.6%

Length

2026-02-11T10:35:32.746290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-11T10:35:32.832186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
phd5071
20.3%
master5036
20.2%
bachelor4973
20.0%
high4920
19.7%
school4920
19.7%

Most occurring characters

ValueCountFrequency (%)
h19884
14.3%
o14813
 
10.6%
r10009
 
7.2%
a10009
 
7.2%
e10009
 
7.2%
c9893
 
7.1%
l9893
 
7.1%
P5071
 
3.6%
D5071
 
3.6%
s5036
 
3.6%
Other values (8)39645
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)139333
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h19884
14.3%
o14813
 
10.6%
r10009
 
7.2%
a10009
 
7.2%
e10009
 
7.2%
c9893
 
7.1%
l9893
 
7.1%
P5071
 
3.6%
D5071
 
3.6%
s5036
 
3.6%
Other values (8)39645
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)139333
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h19884
14.3%
o14813
 
10.6%
r10009
 
7.2%
a10009
 
7.2%
e10009
 
7.2%
c9893
 
7.1%
l9893
 
7.1%
P5071
 
3.6%
D5071
 
3.6%
s5036
 
3.6%
Other values (8)39645
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)139333
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h19884
14.3%
o14813
 
10.6%
r10009
 
7.2%
a10009
 
7.2%
e10009
 
7.2%
c9893
 
7.1%
l9893
 
7.1%
P5071
 
3.6%
D5071
 
3.6%
s5036
 
3.6%
Other values (8)39645
28.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
True
10071 
False
9929 
ValueCountFrequency (%)
True10071
50.4%
False9929
49.6%
2026-02-11T10:35:32.908718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

loan_purpose
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Home
4052 
Business
4034 
Education
4020 
Car
3974 
Personal
3920 

Length

Max length9
Median length8
Mean length6.3971
Min length3

Characters and Unicode

Total characters127942
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEducation
2nd rowBusiness
3rd rowHome
4th rowCar
5th rowHome

Common Values

ValueCountFrequency (%)
Home4052
20.3%
Business4034
20.2%
Education4020
20.1%
Car3974
19.9%
Personal3920
19.6%

Length

2026-02-11T10:35:33.002627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-11T10:35:33.099832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
home4052
20.3%
business4034
20.2%
education4020
20.1%
car3974
19.9%
personal3920
19.6%

Most occurring characters

ValueCountFrequency (%)
s16022
12.5%
e12006
 
9.4%
o11992
 
9.4%
n11974
 
9.4%
a11914
 
9.3%
i8054
 
6.3%
u8054
 
6.3%
r7894
 
6.2%
H4052
 
3.2%
m4052
 
3.2%
Other values (8)31928
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)127942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s16022
12.5%
e12006
 
9.4%
o11992
 
9.4%
n11974
 
9.4%
a11914
 
9.3%
i8054
 
6.3%
u8054
 
6.3%
r7894
 
6.2%
H4052
 
3.2%
m4052
 
3.2%
Other values (8)31928
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)127942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s16022
12.5%
e12006
 
9.4%
o11992
 
9.4%
n11974
 
9.4%
a11914
 
9.3%
i8054
 
6.3%
u8054
 
6.3%
r7894
 
6.2%
H4052
 
3.2%
m4052
 
3.2%
Other values (8)31928
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)127942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s16022
12.5%
e12006
 
9.4%
o11992
 
9.4%
n11974
 
9.4%
a11914
 
9.3%
i8054
 
6.3%
u8054
 
6.3%
r7894
 
6.2%
H4052
 
3.2%
m4052
 
3.2%
Other values (8)31928
25.0%

loan_default
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size976.7 KiB
0
15995 
1
4005 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015995
80.0%
14005
 
20.0%

Length

2026-02-11T10:35:33.225415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-11T10:35:33.308848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
015995
80.0%
14005
 
20.0%

Most occurring characters

ValueCountFrequency (%)
015995
80.0%
14005
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)20000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
015995
80.0%
14005
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
015995
80.0%
14005
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
015995
80.0%
14005
 
20.0%

Interactions

2026-02-11T10:35:26.827003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:20.265137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:21.215023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:22.076055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:22.970499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:23.976517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:25.000882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:25.939780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:26.942176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:20.381538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:21.323797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:22.188490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:23.100207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:24.102044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:25.119978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:26.057936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:27.051842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:20.496701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:21.429579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:22.296381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:23.216740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:24.249253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:25.225327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:26.170445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:27.156794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:20.611529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:21.537312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:22.400481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:23.343282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:24.368875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:25.341904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:26.275001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:27.273712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:20.738903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:21.647011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:22.522092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:23.469918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:24.499989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:25.465191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:26.389740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:27.393475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:20.863919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:21.760968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:22.639275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:23.602945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:24.627552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:25.594316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:26.510340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:27.507738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:20.979287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:21.866941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:22.749690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:23.723010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:24.756793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:25.708373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:26.618315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:27.616827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:21.096993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:21.966069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:22.859061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:23.853333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:24.875668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:25.822083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-11T10:35:26.720577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-11T10:35:33.389718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ageannual_incomecibil_scoredebt_to_income_ratioeducation_levelemployment_typeexisting_loansidentification_throughinterest_rateloan_amountloan_defaultloan_purposeloan_tenure_yearsmarital_statusproperty_owneryears_of_employment
age1.000-0.0040.002-0.0050.0000.0000.0000.000-0.0050.0050.0000.000-0.0050.0000.0080.001
annual_income-0.0041.000-0.0070.0030.0170.0000.0000.006-0.004-0.0000.0000.004-0.0070.0000.0050.004
cibil_score0.002-0.0071.000-0.0050.0000.0150.0080.015-0.0010.0050.0050.0110.0110.0000.021-0.002
debt_to_income_ratio-0.0050.003-0.0051.0000.0060.0000.0000.0000.001-0.0020.0070.0000.0140.0000.0160.004
education_level0.0000.0170.0000.0061.0000.0000.0100.0000.0000.0000.0000.0070.0120.0000.0000.007
employment_type0.0000.0000.0150.0000.0001.0000.0120.0000.0000.0090.0000.0000.0000.0000.0050.010
existing_loans0.0000.0000.0080.0000.0100.0121.0000.0000.0000.0030.0000.0090.0000.0000.0190.000
identification_through0.0000.0060.0150.0000.0000.0000.0001.0000.0060.0130.0000.0060.0080.0060.0040.004
interest_rate-0.005-0.004-0.0010.0010.0000.0000.0000.0061.0000.0000.0000.010-0.0020.0050.0150.006
loan_amount0.005-0.0000.005-0.0020.0000.0090.0030.0130.0001.0000.0000.006-0.0050.0140.0000.000
loan_default0.0000.0000.0050.0070.0000.0000.0000.0000.0000.0001.0000.0050.0000.0040.0000.006
loan_purpose0.0000.0040.0110.0000.0070.0000.0090.0060.0100.0060.0051.0000.0160.0070.0000.015
loan_tenure_years-0.005-0.0070.0110.0140.0120.0000.0000.008-0.002-0.0050.0000.0161.0000.0050.0120.002
marital_status0.0000.0000.0000.0000.0000.0000.0000.0060.0050.0140.0040.0070.0051.0000.0000.008
property_owner0.0080.0050.0210.0160.0000.0050.0190.0040.0150.0000.0000.0000.0120.0001.0000.014
years_of_employment0.0010.004-0.0020.0040.0070.0100.0000.0040.0060.0000.0060.0150.0020.0080.0141.000

Missing values

2026-02-11T10:35:28.326362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-11T10:35:28.562371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

nameidentification_throughcibil_scoreageannual_incomeemployment_typeyears_of_employmentexisting_loansloan_amountloan_tenure_yearsinterest_ratedebt_to_income_ratiomarital_statuseducation_levelproperty_ownerloan_purposeloan_default
0Vihaan SharmaPAN402251698527Self-Employed2501187279713.540.19DivorcedBachelorYesEducation0
1Ishaan PatelPAN735441087238Salaried1526882171111.340.58MarriedMasterYesBusiness0
2Krishna GuptaAadhar57050929749Salaried1126925751113.820.37SingleBachelorYesHome0
3Vihaan SinghPAN40650422413Salaried1512688302015.600.13DivorcedMasterNoCar0
4Aditya MehtaPAN371591268052Self-Employed331367206297.220.49DivorcedBachelorYesHome0
5Ananya SharmaDriving License32057949120Salaried26014751291410.910.60DivorcedMasterNoPersonal1
6Aarav VermaPAN42153689282Business2445931242510.860.17MarriedBachelorYesBusiness0
7Reyansh PatelPAN766402226974Business182109452416.900.11MarriedMasterYesEducation0
8Siya MehtaDriving License514422244970Self-Employed6114245061517.970.37DivorcedHigh SchoolYesBusiness1
9Aarav SinghDriving License63061731596Business1135435831114.180.13DivorcedBachelorYesHome0
nameidentification_throughcibil_scoreageannual_incomeemployment_typeyears_of_employmentexisting_loansloan_amountloan_tenure_yearsinterest_ratedebt_to_income_ratiomarital_statuseducation_levelproperty_ownerloan_purposeloan_default
19990Arjun NairPAN428541672161Business252962361312.130.56MarriedPhDYesBusiness0
19991Ishaan GuptaAadhar735431677126Salaried1516872032312.820.20MarriedBachelorYesPersonal0
19992Ananya NairPassport579351260454Business1421019153137.470.23SingleMasterNoHome0
19993Shaurya VermaDriving License657471750488Salaried17153134739.570.28MarriedPhDNoHome0
19994Ananya SinghPAN551221373483Salaried3902186211414.100.60MarriedHigh SchoolYesEducation0
19995Riya PatelAadhar43448465897Business17111234261314.310.48MarriedHigh SchoolYesPersonal1
19996Riya GuptaPAN822311154778Salaried3434367291613.680.38DivorcedPhDNoCar0
19997Myra IyerPassport618411774308Business441315039259.260.43DivorcedBachelorNoHome1
19998Krishna MehtaPassport37962206630Business1310736672211.590.13SingleHigh SchoolYesHome0
19999Anika NairPAN490521305701Salaried291769648112.780.33DivorcedMasterNoPersonal0